Bayesian Logistic Model for Positive and Unlabeled Data
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-33498-6_11
Reference12 articles.
1. Bekker, J., Davis, J.: Estimating the class prior in positive and unlabeled data through decision tree induction. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence, February 2018
2. Choi, H.M., Hobert, J.P.: The Pólya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic. Electron. J. Statist. 7, 2054–2064 (2013)
3. Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
4. Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2008, pp. 213–220 (2008)
5. Jaskie, K., Elkan, C., Spanias, A.: A modified logistic regression for positive and unlabeled learning. In: 53rd Asilomar Conference on Signals, Systems, and Computers, pp. 2007–2011 (2020)
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